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Record W3178680879 · doi:10.5194/egusphere-egu21-1863

Towards the estimation of Space Weather-related corrosion on pipelines

2021· article· en· W3178680879 on OpenAlexaff
L. Trichtchenko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsPipeline transportCorrosionSpace weatherLatitudeVoltageEnvironmental scienceElectrical engineeringMaterials sciencePhysicsMeteorologyGeologyMetallurgyEngineeringEnvironmental engineeringGeodesy

Abstract

fetched live from OpenAlex

<p>Geomagnetically induced currents (GIC), increased during space weather events, are able to interfere with pipeline corrosion protections systems and potentially can increase corrosion of the pipeline steel.</p><p>Methods, widely used for the evaluation of annual corrosion rates, are based on exposure of steel to constant currents and voltages (DC), or alternating currents and voltages of a constant frequency (50 Hz or 60 Hz), while GIC are characterised by a continuous frequency spectrum, with the range of frequencies from 10<sup>-5</sup> Hz to 1 Hz.</p><p>This paper introduces the methods for use in the estimation of corrosion rates on pipeline steel produced by GIC (commonly referred to as “telluric currents” in the pipeline industry) and provides results calculated for specific time periods with use of available recordings made on pipelines at the times of geomagnetic storms. As well, annual cumulative corrosion rates are estimated based on the modelling of pipeline currents and voltages.</p><p>In addition to the detailed presentation of the methods utilised, a comparison of corrosion rates produced by telluric variations on non-protected and protected pipelines located in mid- and high-latitudes is presented.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.205
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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